CN111907518A - Method for dynamically optimizing AEB braking strategy based on cloud big data analysis - Google Patents
Method for dynamically optimizing AEB braking strategy based on cloud big data analysis Download PDFInfo
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W30/00—Purposes of road vehicle drive control systems not related to the control of a particular sub-unit, e.g. of systems using conjoint control of vehicle sub-units
- B60W30/08—Active safety systems predicting or avoiding probable or impending collision or attempting to minimise its consequences
- B60W30/09—Taking automatic action to avoid collision, e.g. braking and steering
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
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- B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
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- B60—VEHICLES IN GENERAL
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Abstract
The invention discloses a method for dynamically optimizing an AEB braking strategy based on cloud big data analysis, which comprises the following steps: (1) respectively obtaining associated data of road environment, weather environment, vehicle information and cargo allocation information through a cloud analysis platform; (2) the cloud analysis platform forms a real-time optimal braking scheme by using a data fusion method; (3) the driving environment sensing system is used for acquiring front obstacle information, road environment information and vehicle information; (4) forming driving environment perception data by the data collected in the step (3) and transmitting the driving environment perception data to an intelligent ADAS control system; (5) the intelligent ADAS control system forms braking force data according to the data of the driving environment perception system and outputs the braking force data to the braking control system; and acquiring cloud data support. The method can achieve the beneficial effects of dynamically optimizing the braking parameters of the system, breaking the restriction of single-machine calculation, establishing a driving risk prediction model and improving the adaptability of the braking performance of the anti-collision system.
Description
Technical Field
The invention belongs to the technical field of automobiles, and particularly relates to a method for dynamically optimizing an AEB braking strategy based on cloud big data analysis.
Background
An automobile anti-collision system is an intelligent device for preventing the collision of automobiles. The vehicle collision avoidance system can automatically find vehicles, pedestrians or other obstacle objects which may collide with the vehicle, send out an alarm or take measures such as braking or evasion at the same time so as to avoid collision.
The relative speed of the front vehicle and the distance between the two vehicles are automatically controlled by adopting the technologies of millimeter wave radar, laser, sonar, infrared rays, cameras and the like.
After the computer chip processes the distance between the two vehicles and the instantaneous relative speed of the two vehicles, the safety distance of the two vehicles is judged, and if the distance between the two vehicles is smaller than the safety distance, the data processing system sends an instruction; and the other method is that the computer chip calculates the Time To Collision (TTC) of two vehicles to calculate the danger degree, and then gives an alarm and a brake instruction.
With the rapid development of the internet of things, cloud computing, big data and technologies, it becomes possible to collect data of vehicles and the surrounding environment of the vehicles for scene analysis. The development of faster and more efficient communication technologies will meet the requirements for high data bandwidth and transmission speed.
The automobile anti-collision system is combined with the Internet of things, and real-time dynamic optimization of the anti-collision system is possible through big data cloud computing.
The traditional vehicle networking system comprises a vehicle-mounted T-BOX and a cloud background system, wherein the vehicle-mounted T-BOX mainly collects vehicle quantitative vehicle running state data such as vehicle speed, electric quantity, mileage and the like through a CAN bus, CAN be accessed to the Internet through 3G and 4G network cards, and reports the real-time state of the vehicle data to the cloud background system of the vehicle networking in a message form. Meanwhile, the vehicle position information is collected in real time by means of the GPS module and uploaded to the cloud background system.
The technical scheme of the automobile anti-collision system comprises a millimeter wave radar, a laser radar, an infrared radar, a camera and the like. The millimeter wave radar is slightly influenced by natural environment, has moderate detection distance and highest cost performance in the field of vehicle-mounted radars, but is difficult to identify pedestrians, traffic signs and the like; the laser radar has high measurement precision, can be used for establishing a space three-dimensional map in real time, but has high cost and poor effect in rain, snow and fog days; the infrared radar has high measurement precision, mature technology and low cost, but the measurement distance is short (less than 10m), thereby greatly limiting the application scene; the camera is with low costs, can discern the object, is the sensor that functions such as lane departure warning, traffic sign discernment are indispensable, but has the shortcoming such as relying on light, can become invalid, difficult accurate range finding under night and extreme weather.
The traditional automobile anti-collision system is only a fixed braking strategy, carries out real-time acquisition and calculation based on the existing sensing equipment of the automobile, carries out early warning and braking according to a real-time result, has limitation and one-sidedness, and can not carry out more complex operation due to the limited capability of a calculation chip, for example, the best driving scheme, the best alarming scheme and the best collision braking scheme are calculated by combining peripheral driving working conditions on the concrete analysis of complex road conditions. Under the conditions of special road conditions, special vehicle conditions and the like, misjudgment is often caused due to a fixed strategy.
The existing monitoring platform only plays a role in monitoring, and the cloud big data application and the computing function are not embodied at all.
Disclosure of Invention
Aiming at the technical problems in the prior art, the invention provides the method for dynamically optimizing the AEB braking strategy based on cloud big data analysis, which can achieve the beneficial effects of dynamically optimizing the braking parameters of the system, breaking through the restriction of single-machine calculation, establishing a driving risk prediction model and improving the adaptability of the braking performance of the anti-collision system.
In order to solve the technical problems, the invention adopts the technical scheme that: a method for dynamically optimizing an AFB braking strategy based on cloud big data analysis comprises the following steps:
(1) respectively obtaining associated data of road environment, weather environment, vehicle information and cargo allocation information through a cloud analysis platform;
(2) the cloud analysis platform forms vehicle, road and environment parameters by using a data fusion method, dynamically analyzes the data by combining vehicle braking model data to form a real-time optimal braking scheme, and pushes the optimal braking scheme to an intelligent ADAS control system to update braking strategy parameters so as to adapt to real-time vehicle conditions, road conditions and environment changes;
(3) the driving environment sensing system acquires the information of the front obstacle through a radar sensor; collecting road environment information through a vision sensor; collecting vehicle information through a vehicle data analysis module;
(4) forming driving environment perception data by the data collected in the step (3) and transmitting the driving environment perception data to an intelligent ADAS control system;
(5) the intelligent ADAS control system calculates dangerous target information including distance, relative speed and relative position through an internal algorithm module according to the data of the driving environment sensing system, and forms braking force data to be output to the braking control system; meanwhile, the positioning information of the vehicle is uploaded to a cloud analysis platform in real time; and acquiring cloud data support, and forming a complementary strategy with the local and cloud data to dynamically refresh the intelligent ADAS control system.
Preferably, in the step (3), the relative distance, the relative speed and the relative angle data of the front obstacle are collected through a radar sensor such as a millimeter wave radar; collecting road environment information including people, vehicles and lane lines through a visual sensor such as a camera; the vehicle data analysis module is used for collecting the acceleration of the driving direction of the vehicle, the acceleration of the deviation direction and the vehicle condition information, wherein the vehicle condition information comprises the actual speed, the load and the oil consumption.
Compared with the prior art, the invention has the beneficial effects that:
1. the method can dynamically optimize the braking parameters of the vehicle anti-collision system by using cloud data resources.
2. The method can break the restriction of anti-collision single-computer calculation in the prior art and run a local and cloud calculation complementary algorithm.
3. According to the invention, the cloud massive real road driving environment data can be utilized for analysis and excavation, a driving risk prediction model can be established, strategy optimization control is carried out aiming at the vehicle type, the adaptability of the braking performance of the anti-collision system is improved, the occurrence of traffic accidents is effectively avoided, and good economic and social benefits are brought.
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FIG. 1 is a schematic flow chart of the present invention.
Detailed Description
In order to make the technical solutions of the present invention better understood, the present invention will be described in detail below with reference to the accompanying drawings and specific embodiments.
The embodiment of the invention discloses a method for dynamically optimizing an AEB braking strategy based on cloud big data analysis, which comprises the following steps as shown in the figure:
(1) respectively obtaining associated data of road environment, weather environment, vehicle information and cargo allocation information through a cloud analysis platform;
(2) the cloud analysis platform forms vehicle, road and environment parameters by using a data fusion method, dynamically analyzes the data by combining vehicle braking model data to form a real-time optimal braking scheme, and pushes the optimal braking scheme to an intelligent ADAS control system to update braking strategy parameters so as to adapt to real-time vehicle conditions, road conditions and environment changes;
(3) the driving environment sensing system acquires the information of the front obstacle through a radar sensor; collecting road environment information through a vision sensor; collecting vehicle information through a vehicle data analysis module;
(4) forming driving environment perception data by the data collected in the step (3) and transmitting the driving environment perception data to an intelligent ADAS control system;
(5) the intelligent ADAS control system calculates dangerous target information including distance, relative speed and relative position through an internal algorithm module according to the data of the driving environment sensing system, and forms braking force data to be output to the braking control system; meanwhile, the positioning information of the vehicle is uploaded to a cloud analysis platform in real time; and acquiring cloud data support, and forming a complementary strategy with the local and cloud data to dynamically refresh the intelligent ADAS control system.
In this embodiment, in step (3), data of a relative distance, a relative speed, and a relative angle of the obstacle ahead are collected by a radar sensor such as a millimeter wave radar; collecting road environment information including people, vehicles and lane lines through a visual sensor such as a camera; the vehicle data analysis module is used for collecting the acceleration of the driving direction of the vehicle, the acceleration of the deviation direction and the vehicle condition information, wherein the vehicle condition information comprises the actual speed, the load and the oil consumption.
In this embodiment, the cloud analysis platform can be extended continuously, so as to enrich the cloud data resources, integrate more environmental factors into the algorithm model, and take more interference factors into consideration, thereby improving the adaptability of the brake control parameters.
In this embodiment, through local and high in the clouds dual data analysis, optimize anticollision braking control system's control parameter, update AEB controller braking strategy in real time, provide more adaptive parameter for the emergency braking of vehicle and carry out effective braking.
The present invention has been described in detail with reference to the embodiments, but the description is only illustrative of the present invention and should not be construed as limiting the scope of the present invention. The scope of the invention is defined by the claims. The technical solutions of the present invention or those skilled in the art, based on the teaching of the technical solutions of the present invention, should be considered to be within the scope of the present invention, and all equivalent changes and modifications made within the scope of the present invention or equivalent technical solutions designed to achieve the above technical effects are also within the scope of the present invention. It should be noted that for the sake of clarity, parts of the description of the invention have been omitted where there is no direct explicit connection with the scope of protection of the invention, but where components and processes are known to those skilled in the art.
Claims (2)
1. A method for dynamically optimizing an AEB braking strategy based on cloud big data analysis is characterized by comprising the following steps:
(1) respectively obtaining associated data of road environment, weather environment, vehicle information and cargo allocation information through a cloud analysis platform;
(2) the cloud analysis platform forms vehicle, road and environment parameters by using a data fusion method, dynamically analyzes data by combining vehicle braking model data to form a real-time optimal braking scheme, and pushes the optimal braking scheme to an intelligent ADAS control system to update braking strategy parameters;
(3) the driving environment sensing system acquires the information of the front obstacle through a radar sensor; collecting road environment information through a vision sensor; collecting vehicle information through a vehicle data analysis module;
(4) forming driving environment perception data by the data collected in the step (3) and transmitting the driving environment perception data to an intelligent ADAS control system;
(5) the intelligent ADAS control system calculates dangerous target information including distance, relative speed and relative position through an internal algorithm module according to the data of the driving environment sensing system, and forms braking force data to be output to the braking control system; meanwhile, the positioning information of the vehicle is uploaded to a cloud analysis platform in real time; and acquiring cloud data support, and forming a complementary strategy with the local and cloud data to dynamically refresh the intelligent ADAS control system.
2. The method for dynamically optimizing the AEB braking strategy based on cloud big data analysis according to claim 1, wherein in the step (3), data of relative distance, relative speed and relative angle of a front obstacle are collected through a radar sensor; collecting road environment information including people, vehicles and lane lines through a vision sensor; the vehicle data analysis module is used for collecting the acceleration of the driving direction of the vehicle, the acceleration of the deviation direction and the vehicle condition information, wherein the vehicle condition information comprises the actual speed, the load and the oil consumption.
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